Do factors other than need determine utilization of physicians' services in Ontario?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Universal health care systems seek to ensure access to care on the basis of need, rather than income, but there are concerns about preferential access to cardiovascular and specialist care for high income patients. In this study, I used population-based, individual-level health, income and utilization data to determine whether whether there is evidence for differential access to physician care in relation to household income. METHODS: I studied data for 2170 Ontario respondents to the 1995 National Population Health Survey (aged 40 to 79 years) who had approved linkage of their survey responses to the administrative databases of the Ontario Health Insurance Plan and for whom income data were available. I used linear and generalized linear regression to model the mean per capita expenditures on physician care and the probability of referral to a specialist in relation to income and self-reported health status. RESULTS: Residents of higher income households incurred lower per capita expenditures for physicians' services than those in lower income households; for example, the mean per capita expenditure in the upper middle income group was $220 less (95% confidence interval -$87 to -$334) than the mean per capita expenditure in the lowest income group. Expenditures were significantly related to self-reported health status; for example, the mean per capita expenditure among those reporting fair health status was $590 higher (95% confidence interval $465 to $737) than among those reporting excellent health. After adjustment for health status, there was no association between income and the expenditures on all physician services, out-of-hospital services or specialist care. INTERPRETATION: Utilization of physicians' services in Ontario is based on need, rather than income.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it